Global features only capture the overall property of an object but do not contain information about local properties.
Local features are the most essential part of this network.
The local features carry the neighborhood properties as well as surface properties such as normals.
Local features prove to be very helpful in tasks like point cloud segmentation or surface estimation.
We have PointNet [1]} that extracts features per point by using a series of MLP.
But it does not induce local features based on distance metrics.
An improvement to PointNet, PointNet++ [2]} captures patches of neighborhood points for every point in the input set and performs a series of MLP on every patch and captures local information.
We use a series of sample and grouping modules at different scales like in PointNet++ to extract hierarchical features and combine all those information into a single feature layer.
This is also called multi-resolution grouping.
For an input point cloud \(X_p\)  with shape \(n \times 6\) , the output of local features \(X_l\)  is \(n \times l\)  where \(l\)  is the number of features extracted in the last MLP layer of local features module.
